def createModel(targetGraph, startDate, endDate, recordStep, M, matchAlpha, breakSize, matchAlg, theta=None):
alpha = 2
zeroVal = 0.9
numpy.random.seed(21)
graph = targetGraph.subgraph(targetGraph.removedIndsAt(startDate))
graph.addVertices(M-graph.size)
logging.debug("Created graph: " + str(graph))
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
featureInds = numpy.ones(graph.vlist.getNumFeatures(), numpy.bool)
featureInds[HIVVertices.dobIndex] = False
featureInds[HIVVertices.infectionTimeIndex] = False
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
matcher = GraphMatch(matchAlg, alpha=matchAlpha, featureInds=featureInds, useWeightM=False)
graphMetrics = HIVGraphMetrics2(targetGraph, breakSize, matcher, startDate)
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
if theta != None:
model.setParams(theta)
return model
def runModel(meanTheta):
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.toySimulationParams()
endDate = 1000.0
recordStep = 50
undirected = True
logging.debug("MeanTheta=" + str(meanTheta))
numReps = 10
numInfectedIndices = []
numRemovedIndices = []
numRemovedEdges = []
numContactEdges = []
statistics = GraphStatistics()
statsTimes = numpy.arange(0, endDate, recordStep)
for i in range(numReps):
graph = HIVGraph(M, undirected)
logging.info("Created graph at index " + str(i) + ": " + str(graph))
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates)
model.setT0(startDate)
model.setT(endDate)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats = HIVModelUtils.generateStatistics(
graph, statsTimes
)
numInfectedIndices.append([len(x) for x in infectedIndices])
numRemovedIndices.append([len(x) for x in removedIndices])
numContactEdges.append(contactGraphStats[:, statistics.numVerticesIndex])
numRemovedEdges.append(removedGraphStats[:, statistics.numVerticesIndex])
numInfectedIndices = numpy.array(numInfectedIndices)
numInfectedIndices = numpy.mean(numInfectedIndices, 0)
numRemovedIndices = numpy.array(numRemovedIndices)
numRemovedIndices = numpy.mean(numRemovedIndices, 0)
numContactEdges = numpy.array(numContactEdges)
numContactEdges = numpy.mean(numContactEdges, 0)
numRemovedEdges = numpy.array(numRemovedEdges)
numRemovedEdges = numpy.mean(numRemovedEdges, 0)
return statsTimes, numInfectedIndices, numRemovedIndices, numContactEdges, numRemovedEdges, vertexArray[:, 6]
def simulateModel(theta):
"""
The parameter t is the particle index.
"""
logging.debug("theta=" + str(theta))
#We start with the observed graph at the start date
graph = targetGraph.subgraph(targetGraph.removedIndsAt(startDate))
graph.addVertices(M-graph.size)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
featureInds = numpy.ones(graph.vlist.getNumFeatures(), numpy.bool)
featureInds[HIVVertices.dobIndex] = False
featureInds[HIVVertices.infectionTimeIndex] = False
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
matcher = GraphMatch(matchAlg, alpha=matchAlpha, featureInds=featureInds, useWeightM=False)
graphMetrics = HIVGraphMetrics2(targetGraph, breakSize, matcher, float(endDate))
recordStep = (endDate-startDate)/float(numRecordSteps)
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(theta)
model.simulate()
objective = model.objective()
return objective
def setUp(self):
numpy.random.seed(21)
numpy.set_printoptions(suppress=True, precision=4)
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
M = 100
undirected = True
self.graph = HIVGraph(M, undirected)
s = 3
self.gen = scipy.stats.zipf(s)
hiddenDegSeq = self.gen.rvs(size=self.graph.getNumVertices())
rates = HIVRates(self.graph, hiddenDegSeq)
self.model = HIVEpidemicModel(self.graph, rates)
def profileSimulate(self):
startDate, endDates, numRecordSteps, M, targetGraph = HIVModelUtils.realSimulationParams()
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
undirected = True
graph = HIVGraph(M, undirected)
logging.info("Created graph: " + str(graph))
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates)
model.setT0(startDate)
model.setT(startDate+1000)
model.setRecordStep(10)
model.setParams(meanTheta)
logging.debug("MeanTheta=" + str(meanTheta))
ProfileUtils.profile('model.simulate()', globals(), locals())
def testSimulate2(self):
alpha = 2
zeroVal = 0.9
startDate = 0.0
endDate = 200.0
M = 1000
undirected = True
theta, sigmaTheta, pertTheta = HIVModelUtils.toyTheta()
numpy.random.seed(21)
graph = HIVGraph(M, undirected)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=None)
#model.setRecordStep(recordStep)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
numVertices = graph.size
numEdges = graph.getNumEdges()
#Try again
numpy.random.seed(21)
graph = HIVGraph(M, undirected)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=None)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
numVertices2 = graph.size
numEdges2 = graph.getNumEdges()
self.assertEquals(numVertices2, numVertices)
self.assertEquals(numEdges2, numEdges)
def runModel(theta, endDate=100.0, M=1000):
numpy.random.seed(21)
undirected= True
recordStep = 10
startDate = 0
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
graph = HIVGraph(M, undirected)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
logging.debug("MeanTheta=" + str(theta))
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate)
model.setRecordStep(recordStep)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
return times, infectedIndices, removedIndices, graph, model
graphList = []
numInfected = numpy.zeros(numRepetitions)
numRemoved = numpy.zeros(numRepetitions)
for j in range(numRepetitions):
graph = HIVGraph(M, undirected)
logging.debug("Created graph: " + str(graph))
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates)
model.setT(endDate)
model.setRecordStep(recordStep)
model.setParams(theta)
logging.debug("Theta = " + str(theta))
times, infectedIndices, removedIndices, graph = model.simulate(True)
graphFileName = outputDir + "ToyEpidemicGraph" + str(j)
graph.save(graphFileName)
graphList.append(graph)
numInfected[j] = len(graph.getInfectedSet())
numRemoved[j] = len(graph.getRemovedSet())
logging.debug("Infected (mean, std): " + str((numpy.mean(numInfected), numpy.std(numInfected))))
def testSimulate(self):
T = 1.0
self.graph.getVertexList().setInfected(0, 0.0)
self.model.setT(T)
times, infectedIndices, removedIndices, graph = self.model.simulate(verboseOut=True)
numInfects = 0
for i in range(graph.getNumVertices()):
if graph.getVertex(i)[HIVVertices.stateIndex] == HIVVertices==infected:
numInfects += 1
self.assertTrue(numInfects == 0 or times[len(times)-1] >= T)
#Test with a larger population as there seems to be an error when the
#number of infectives becomes zero.
M = 100
undirected = True
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
self.graph.removeAllEdges()
T = 21.0
hiddenDegSeq = self.gen.rvs(size=self.graph.getNumVertices())
rates = HIVRates(self.graph, hiddenDegSeq)
model = HIVEpidemicModel(self.graph, rates)
model.setRecordStep(10)
model.setT(T)
#Test detection rates
print("Starting test")
T = 1000.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.contactRate = 0
rates.randDetectRate = 0.1
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
print(times)
self.assertEquals(len(infectedIndices[0]), 10)
self.assertEquals(len(removedIndices[0]), 0)
T = 10.0
graph.removeAllEdges()
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.randDetectRate = 0.0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
self.assertEquals(len(removedIndices[-1]), 0)
T = 100.0
graph.removeAllEdges()
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.randDetectRate = 10.0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
self.assertEquals(len(removedIndices[-1]), 10)
#Test contact tracing
T = 1000.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.randDetectRate = 0.01
rates.ctRatePerPerson = 0.5
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
self.assertTrue((graph.vlist.V[:, HIVVertices.detectionTypeIndex] == HIVVertices.contactTrace).sum() > 0)
#Test contact rate
print("Testing contact rate")
contactRates = [0.5, 1, 2, 4]
numContacts = numpy.zeros(len(contactRates))
for i, contactRate in enumerate(contactRates):
T = 100.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(1)
print(i, graph.vlist.V[graph.getInfectedSet().pop(), :])
rates = HIVRates(graph, hiddenDegSeq)
rates.contactRate = contactRate
rates.infectProb = 0.0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
numContacts[i] = model.numContacts
lastN = -1
for i, n in enumerate(numContacts):
#This is an odd case in which we have a bisexual woman, there are no contacts
#since they are not modelled
if n != 0:
self.assertTrue(n > lastN)
#Test infection rate
print("Testing infection probability")
infectProbs = [0.01, 0.1, 0.2, 0.5]
numInfects = numpy.zeros(len(contactRates))
for i, infectProb in enumerate(infectProbs):
T = 100.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.contactRate = 0.5
rates.infectProb = infectProb
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
numInfects[i] = len(graph.getInfectedSet())
for n in numInfects:
self.assertTrue(n > lastN)
print("Testing contact paramters")
alphas = 1-numpy.array([0.01, 0.1, 0.2, 0.5, 0.99])
edges = numpy.zeros(len(alphas))
for i, alpha in enumerate(alphas):
T = 100.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(1)
rates = HIVRates(graph, hiddenDegSeq)
rates.setAlpha(alpha)
rates.infectProb = 0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
edges[i] = graph.getNumEdges()
self.assertEquals(edges[0], 1)
self.assertTrue((numpy.diff(edges) > 0).all())
class HIVEpidemicModelTest(unittest.TestCase):
def setUp(self):
numpy.random.seed(21)
numpy.set_printoptions(suppress=True, precision=4)
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
M = 100
undirected = True
self.graph = HIVGraph(M, undirected)
s = 3
self.gen = scipy.stats.zipf(s)
hiddenDegSeq = self.gen.rvs(size=self.graph.getNumVertices())
rates = HIVRates(self.graph, hiddenDegSeq)
self.model = HIVEpidemicModel(self.graph, rates)
@unittest.skip("")
def testSimulate(self):
T = 1.0
self.graph.getVertexList().setInfected(0, 0.0)
self.model.setT(T)
times, infectedIndices, removedIndices, graph = self.model.simulate(verboseOut=True)
numInfects = 0
for i in range(graph.getNumVertices()):
if graph.getVertex(i)[HIVVertices.stateIndex] == HIVVertices==infected:
numInfects += 1
self.assertTrue(numInfects == 0 or times[len(times)-1] >= T)
#Test with a larger population as there seems to be an error when the
#number of infectives becomes zero.
M = 100
undirected = True
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
self.graph.removeAllEdges()
T = 21.0
hiddenDegSeq = self.gen.rvs(size=self.graph.getNumVertices())
rates = HIVRates(self.graph, hiddenDegSeq)
model = HIVEpidemicModel(self.graph, rates)
model.setRecordStep(10)
model.setT(T)
#Test detection rates
print("Starting test")
T = 1000.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.contactRate = 0
rates.randDetectRate = 0.1
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
print(times)
self.assertEquals(len(infectedIndices[0]), 10)
self.assertEquals(len(removedIndices[0]), 0)
T = 10.0
graph.removeAllEdges()
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.randDetectRate = 0.0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
self.assertEquals(len(removedIndices[-1]), 0)
T = 100.0
graph.removeAllEdges()
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.randDetectRate = 10.0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
self.assertEquals(len(removedIndices[-1]), 10)
#Test contact tracing
T = 1000.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.randDetectRate = 0.01
rates.ctRatePerPerson = 0.5
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
self.assertTrue((graph.vlist.V[:, HIVVertices.detectionTypeIndex] == HIVVertices.contactTrace).sum() > 0)
#Test contact rate
print("Testing contact rate")
contactRates = [0.5, 1, 2, 4]
numContacts = numpy.zeros(len(contactRates))
for i, contactRate in enumerate(contactRates):
T = 100.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(1)
print(i, graph.vlist.V[graph.getInfectedSet().pop(), :])
rates = HIVRates(graph, hiddenDegSeq)
rates.contactRate = contactRate
rates.infectProb = 0.0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
numContacts[i] = model.numContacts
lastN = -1
for i, n in enumerate(numContacts):
#This is an odd case in which we have a bisexual woman, there are no contacts
#since they are not modelled
if n != 0:
self.assertTrue(n > lastN)
#Test infection rate
print("Testing infection probability")
infectProbs = [0.01, 0.1, 0.2, 0.5]
numInfects = numpy.zeros(len(contactRates))
for i, infectProb in enumerate(infectProbs):
T = 100.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(10)
rates = HIVRates(graph, hiddenDegSeq)
rates.contactRate = 0.5
rates.infectProb = infectProb
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
numInfects[i] = len(graph.getInfectedSet())
for n in numInfects:
self.assertTrue(n > lastN)
print("Testing contact paramters")
alphas = 1-numpy.array([0.01, 0.1, 0.2, 0.5, 0.99])
edges = numpy.zeros(len(alphas))
for i, alpha in enumerate(alphas):
T = 100.0
graph = HIVGraph(M, undirected)
graph.setRandomInfected(1)
rates = HIVRates(graph, hiddenDegSeq)
rates.setAlpha(alpha)
rates.infectProb = 0
model = HIVEpidemicModel(graph, rates)
model.setT(T)
times, infectedIndices, removedIndices, graph = model.simulate(verboseOut=True)
edges[i] = graph.getNumEdges()
self.assertEquals(edges[0], 1)
self.assertTrue((numpy.diff(edges) > 0).all())
def testSimulate2(self):
alpha = 2
zeroVal = 0.9
startDate = 0.0
endDate = 200.0
M = 1000
undirected = True
theta, sigmaTheta, pertTheta = HIVModelUtils.toyTheta()
numpy.random.seed(21)
graph = HIVGraph(M, undirected)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=None)
#model.setRecordStep(recordStep)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
numVertices = graph.size
numEdges = graph.getNumEdges()
#Try again
numpy.random.seed(21)
graph = HIVGraph(M, undirected)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=None)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
numVertices2 = graph.size
numEdges2 = graph.getNumEdges()
self.assertEquals(numVertices2, numVertices)
self.assertEquals(numEdges2, numEdges)
@unittest.skip("")
def testSimulateInfects(self):
#Test varying infection probabilities
heteroContactRate = 0.1
manWomanInfectProb = 1.0
meanTheta = numpy.array([100, 1, 1, 0, 0, heteroContactRate, manWomanInfectProb], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
newInfects = numpy.setdiff1d(graph.getInfectedSet(), numpy.array(infectedIndices[0]))
self.assertTrue((graph.vlist.V[newInfects, HIVVertices.genderIndex] == HIVVertices.female).all())
manWomanInfectProb = 0.1
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, 0, manWomanInfectProb, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
newInfects2 = numpy.setdiff1d(graph.getInfectedSet(), numpy.array(infectedIndices[0]))
self.assertTrue((graph.vlist.V[newInfects2, HIVVertices.genderIndex] == HIVVertices.female).all())
self.assertTrue(newInfects.shape[0] > newInfects2.shape[0])
#Now only women infect
heteroContactRate = 0.1
womanManInfectProb = 1.0
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, womanManInfectProb, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
newInfects = numpy.setdiff1d(graph.getInfectedSet(), numpy.array(infectedIndices[0]))
self.assertTrue((graph.vlist.V[newInfects, HIVVertices.genderIndex] == HIVVertices.male).all())
womanManInfectProb = 0.1
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, womanManInfectProb, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
newInfects2 = numpy.setdiff1d(graph.getInfectedSet(), numpy.array(infectedIndices[0]))
self.assertTrue((graph.vlist.V[newInfects2, HIVVertices.genderIndex] == HIVVertices.male).all())
self.assertTrue(newInfects.shape[0] > newInfects2.shape[0])
@unittest.skip("")
def testSimulateDetects(self):
heteroContactRate = 0.05
endDate = 100
randDetectRate = 0
meanTheta = numpy.array([100, 0.95, 1, 1, randDetectRate, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
detectedSet = graph.getRemovedSet()
self.assertEquals(len(detectedSet), 0)
heteroContactRate = 0.0
randDetectRate = 0.01
meanTheta = numpy.array([100, 0.95, 1, 1, randDetectRate, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
detectedSet = graph.getRemovedSet()
self.assertTrue(len(detectedSet) < 100*randDetectRate*endDate)
randDetectRate = 0.005
meanTheta = numpy.array([100, 0.95, 1, 1, randDetectRate, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
detectedSet2 = graph.getRemovedSet()
print(len(detectedSet), len(detectedSet2))
self.assertTrue(abs(len(detectedSet)*2 - len(detectedSet2))<15)
removedGraph = graph.subgraph(list(graph.getRemovedSet()))
edges = removedGraph.getAllEdges()
for edge in edges:
i, j = edge
self.assertEquals(removedGraph.vlist.V[i, HIVVertices.detectionTimeIndex]. HIVVertices.randomDetect)
self.assertEquals(removedGraph.vlist.V[j, HIVVertices.detectionTimeIndex]. HIVVertices.randomDetect)
#Test contact tracing
randDetectRate = 0
setCtRatePerPerson = 0.1
meanTheta = numpy.array([100, 0.95, 1, 1, randDetectRate, setCtRatePerPerson, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
detectedSet = graph.getRemovedSet()
self.assertEquals(len(detectedSet), 0)
randDetectRate = 0.001
setCtRatePerPerson = 0.1
meanTheta = numpy.array([100, 0.95, 1, 1, randDetectRate, setCtRatePerPerson, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta, endDate=500.0)
detectedSet = graph.getRemovedSet()
removedGraph = graph.subgraph(list(graph.getRemovedSet()))
edges = removedGraph.getAllEdges()
for i in removedGraph.getAllVertexIds():
if removedGraph.vlist.V[i, HIVVertices.detectionTypeIndex] == HIVVertices.contactTrace:
self.assertTrue(removedGraph.vlist.V[i, HIVVertices.detectionTimeIndex] >= 180)
@unittest.skip("")
def testFindStandardResults(self):
times = [3, 12, 22, 25, 40, 50]
infectedIndices = [[1], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]
removedIndices = [[1], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]
self.model.setT(51.0)
self.model.setRecordStep(10)
times, infectedIndices, removedIndices = self.model.findStandardResults(times, infectedIndices, removedIndices)
self.assertTrue((numpy.array(times)==numpy.arange(0, 60, 10)).all())
#Now try case where simulation is slightly longer than T and infections = 0
numpy.random.seed(21)
times = [3, 12, 22, 25, 40, 50]
infectedIndices = [[1], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]
removedIndices = [[1], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2]]
self.model.setT(51.0)
self.model.setRecordStep(10)
times, infectedIndices, removedIndices = self.model.findStandardResults(times, infectedIndices, removedIndices)
logging.debug(times)